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source("tianfengRwrappers.R")

整合算法可能出现负值，运行SCENIC时舍弃了这些异常值

#提取SMC细胞亚群
SMCs_list <- list(ds0,ds2)

SMCs_list <- lapply(X = SMCs_list, FUN = function(x) {
    x <- NormalizeData(x)
    x <- FindVariableFeatures(x, selSMCtion.method = "vst", nfeatures = 2000)
})

Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Warning: The following arguments are not used: selSMCtion.method
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Warning: The following arguments are not used: selSMCtion.method
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

#需要分析的差异基因
int_features <- SelectIntegrationFeatures(object.list = SMCs_list)
#选择合并的anchor特征
int_anchors <- FindIntegrationAnchors(object.list = SMCs_list, anchor.features = int_features)

Scaling features for provided objects

  |                                                  | 0 % ~calculating  
  |+++++++++++++++++++++++++                         | 50% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s  

Finding all pairwise anchors

  |                                                  | 0 % ~calculating  

Running CCA

SMCs_combined <- ScaleData(SMCs_combined, verbose = FALSE)
SMCs_combined <- RunPCA(SMCs_combined, npcs = 30, verbose = FALSE)
SMCs_combined <- RunUMAP(SMCs_combined, reduction = "pca", dims = 1:30)

SMCs_combined <- FindNeighbors(SMCs_combined, reduction = "pca", dims = 1:30)
SMCs_combined <- FindClusters(SMCs_combined, resolution = 0.1) # resolution 取0.1 或 0.2
umapplot(SMCs_combined)

SMCs_combined <- FindClusters(SMCs_combined, resolution = 0.2) # resolution 取0.1 或 0.2
umapplot(SMCs_combined)

umapplot(SMCs_combined, split.by = "conditions")
# Idents(SMCs_combined) <- SMCs_combined$orig.ident 
# # SMCs_combined <- RenameIdents(SMCs_combined,
#                                '1'='coronary arteries','2'='coronary arteries',
#                               '3'='coronary arteries','4'='coronary arteries',
#                               '5'='coronary arteries','6'='coronary arteries',
#                               '7'='coronary arteries','8'='coronary arteries',
#                               'CA_sample1.txt'='carotid arteries',
#                               'CA_sample2.txt'='carotid arteries','CA_sample3.txt'='carotid arteries',)
# SMCs_combined$conditions <- Idents(SMCs_combined)

# Idents(SMCs_combined) <- SMCs_combined$conditions
# ds0_SMC <- merge(subset(SMCs_combined,ident = "NA"),subset(SMCs_combined,ident = "AC"))
# ds0_SMC@reductions[["umap"]] <- SMCs_combined@reductions[["umap"]]

multi_featureplot(c("DCN","LUM","MMP2","ACTA2"),SMCs_combined,labels = "",label = F,min.cutoff = 0)

negative values

datamat[datamat>0] <- 0
datamat[datamat<0] <- 1


pheatmap::pheatmap(datamat, color = c("#FFFFFF", "#000000"),
        border_color = NA, cluster_rows = T, cluster_cols = FALSE,
        main = "CCA data", show_rownames = F,show_colnames = F)

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